infer_unknown_changepoints

input_data

l_max

depth

symbols appearing in the sequence.

alphabet

number of iterations; for more information see <a href="https://arxiv.org/pdf/2203.04341.pdf">Lungu et al. (2022)</a>.

iters

file path for storing the results.

fileName

This function implements the Metropolis-Hastings sampling algorithm for inferring the number of change-points and their locations.

An implementation of a collection of tools for exact Bayesian inference with discrete times series. This package contains functions that can be used for prediction, model selection, estimation, segmentation/change-point detection and other statistical tasks. Specifically, the functions provided can be used for the exact computation of the prior predictive likelihood of the data, for the identification of the a posteriori most likely (MAP) variable-memory Markov models, for calculating the exact posterior probabilities and the AIC and BIC scores of these models, for prediction with respect to log-loss and 0-1 loss and segmentation/change-point detection. Example data sets from finance, genetics, animal communication and meteorology are also provided. Detailed descriptions of the underlying theory and algorithms can be found in [Kontoyiannis et al. 'Bayesian Context Trees: Modelling and exact inference for discrete time series.' Journal of the Royal Statistical Society: Series B (Statistical Methodology), April 2022. Available at: <arXiv:2007.14900> [stat.ME], July 2020] and [Lungu et al. 'Change-point Detection and Segmentation of Discrete Data using Bayesian Context Trees' <arXiv:2203.04341> [stat.ME], March 2022].

Valentinian Lungu

Bayesian Context Trees for Discrete Time Series

infer_unknown_changepoints function

number of iterations; for more information see <a href='https://arxiv.org/pdf/2203.04341.pdf'>Lungu et al. (2022)</a>.

infer_unknown_changepoints: Inferring the number of change-points and their locations.

Description

Usage

Arguments

Value

See Also

Examples